IntegraMind: An Intelligent Framework for Unified Assessment and Intervention in Dual Diagnosis

Mental health disorders and substance use disorders (SUDs) frequently co-occur, yet digital interventions typically address them separately. This paper presents a novel graph-based chatbot framework that simultaneously addresses these co-occurring conditions through a unified system. Our framework employs a multi- agent architecture implemented with LangGraph, where specialized agents handle distinct conversation aspects while collaborating to provide comprehensive support. The system integrates validated screening tools directly into the conversation flow, uses Retrieval-Augmented Generation (RAG) to deliver evidence-based therapeutic content, and implements human-in-the-loop safety protocols that automatically alert therapists when crisis indicators are detected. Our comprehensive evaluation across 492 synthetic test cases demonstrates 96.88% screening accuracy, 93.33% crisis detection recall with 97.22% precision, significantly exceeding existing system performance. Our approach aims to overcome limitations of existing chatbots by combining systematic assessment, knowledge graph-based reasoning, therapeutic content retrieval, and explicit escalation protocols across both mental health and substance use domains.

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Publication Details

Journal
International Journal of Computational Intelligence Systems
Published
2026-03-28
DOI
https://doi.org/10.1007/s44196-026-01276-1
Primary Topic
Digital Mental Health Interventions
Type
article
Field-Weighted Citation Impact
0.00
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article

IntegraMind: An Intelligent Framework for Unified Assessment and Intervention in Dual Diagnosis

Arun Agarwal, Ramanarayan Ransingh
International Journal of Computational Intelligence Systems
Digital Mental Health Interventions
article

IntegraMind: An Intelligent Framework for Unified Assessment and Intervention in Dual Diagnosis

Arun Agarwal, Ramanarayan Ransingh
article en

Abstract

Mental health disorders and substance use disorders (SUDs) frequently co-occur, yet digital interventions typically address them separately. This paper presents a novel graph-based chatbot framework that simultaneously addresses these co-occurring conditions through a unified system. Our framework employs a multi- agent architecture implemented with LangGraph, where specialized agents handle distinct conversation aspects while collaborating to provide comprehensive support. The system integrates validated screening tools directly into the conversation flow, uses Retrieval-Augmented Generation (RAG) to deliver evidence-based therapeutic content, and implements human-in-the-loop safety protocols that automatically alert therapists when crisis indicators are detected. Our comprehensive evaluation across 492 synthetic test cases demonstrates 96.88% screening accuracy, 93.33% crisis detection recall with 97.22% precision, significantly exceeding existing system performance. Our approach aims to overcome limitations of existing chatbots by combining systematic assessment, knowledge graph-based reasoning, therapeutic content retrieval, and explicit escalation protocols across both mental health and substance use domains.

International Journal of Computational Intelligence Systems
Siksha O Anusandhan University (IN)
Good health and well-being
Openalex Percentile: Top 5%
Digital Mental Health Interventions
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IntegraMind: An Intelligent Framework for Unified Assessment and Intervention in Dual Diagnosis — Arun Agarwal, Ramanarayan Ransingh · International Journal of Computational Intelligence Systems (2026) | TGRS Research Map | TGRS